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WARNING: This wiki is outdated. Please refer to https://github.com/smangul1/rop for the latest information about using ROP.
ROP discovers the source of unmapped reads, which originated from complex RNA molecules, recombinant B and T cell receptors and microbial communities.
Download ROP using
git clone https://github.com/smangul1/rop.git
Install ROP from the base directory
cd rop
./install.sh
Download reference database
./getDB.py ~/
Run ROP analysis by a single command
./rop.py example/unmappedExample.fastq example/ropOut/
Find ROP analysis in example/ropOut/ directory. Learn more about ROP output here
Use the sidebar on the right to navigate ROP tutorial. Get started with a toy example of 2000 reads distributed with ROP package.
ROP is a computational protocol aimed to discover the source of all unmapped, which originate from complex RNA molecules, recombinant B and T cell receptors and microbial communities. We have tested ROP on 1 trillion reads from 10641 RNA-Seq samples across at least 54 tissues and 2630 individuals. The ROP accounts for 99.9% of all reads, compared to 82.9% by conventional mapping-based protocols. ROP is able to profile:
- repeats
- hyper-edited RNAs
- circRNAs, gene fusions, trans-splicing events
- recombined B and T cell receptor repertoires
- microbial communities
The 'dumpster diving' profile of unmapped reads output by our method is not limited to RNA-Seq technology and may be applied to whole-exome and whole-genome sequencing.
Mangul, Serghei, et al. "ROP: Dumpster Diving in RNA-sequencing to find the source of 1 trillion reads across diverse adult human tissues." Genome biology 19.1 (2018): 36.
Slides from my talk at ASHG are available here
Please do not hesitate to contact us ([email protected]) if you have any comments, suggestions, or clarification requests regarding the tutorial or if you would like to contribute to this resource.
See the LICENCE.txt file